AWS Batch now supports quota management and preemption for SageMaker Training jobs
AWS Batch now supports quota management with job preemption for SageMaker Training jobs, enabling you to efficiently allocate and share compute resources across your teams and projects. If you're using GPU capacity in SageMaker Training jobs, you can now intelligently allocate compute resources, prioritize your business-critical training jobs, and automatically preempt lower-priority workloads when your urgent experiments arrive.
With quota management, you can create up to 20 quota shares per job queue that function as virtual queues with dedicated capacity limits and configurable resource sharing strategies. The service automatically uses cross-share preemption to restore borrowed capacity when the original owner submits jobs, and supports in-share preemption to allow high-priority jobs to preempt lower-priority jobs within the same quota share. You can monitor capacity utilization at the queue, quota share, and job-level granularity, update job priorities after submission to influence preemption decisions, and configure preemption retry limits to control behavior. The feature integrates directly with the SageMaker Python SDK via the aws_batch module.
Quota management with job preemption for SageMaker Training jobs is available today in all AWS Regions where AWS Batch is available. For more information, see our Quota Management example notebook on GitHub and the AWS Batch User Guide.